CO2 Breakthrough Research Articles

Caprock sealing integrity and key indicators of CO2 geological storage considering the effect of hydraulic-mechanical coupling: X field in the Bohai Bay Basin, China

Caprock sealing efficiency is an essential guarantee for the long-term safety and stability of CO2 geological storage (CCS). However, the uncertainty in the physical and mechanical properties of deep formations poses challenges to accurately predict the risks of CO2 leakage resulting in CO2 breakthrough or caprock fracture. This study aims to address the issue of imperfect key indicators for caprock sealing by focusing on the CCS project in the Bohai Bay Basin, China. A hydraulic-mechanical (HM) coupling program without considering multiphase flow and the chemical reactions is secondary developed based on the finite element software ABAQUS. Furthermore, a three-dimensional finite element numerical model is established for the analysis of HM coupling, with pore pressure increment (∆PP), Coulomb failure stress (∆CFS) and displacement (U) as evaluation criteria. The Tornado analysis and response surface analysis are employed to analyze the impact of 17 indicators on the caprock sealing, including caprock thickness, burial depth, reservoir and caprock permeability parameters, and mechanical parameters. Subsequently, key performance indicators for caprock sealing are determined. The research results indicate that the reservoir permeability, injection rate, caprock permeability, caprock Young's modulus, caprock internal friction angle, caprock Poisson's ratio, and caprock burial depth are key indicators of caprock sealing capability. The reservoir permeability has a greater impact sensitivity compared to the caprock permeability. Pore pressure and displacement increase with increasing in reservoir permeability. The caprock's resistance to deformation and fracturing increases with increasing in Young's modulus and Poisson's ratio of caprock. This study provides valuable insights for evaluating caprock sealing during CO2 storage in saline aquifers.

Hierarchically N-doped porous carbon synthesized from 3D cellulose alcogel decorated by in-situ growth of ZIF-8 for high performance CO2 capture

The physical properties and chemical compositions of porous carbons are crucial factors in determining their effectiveness in adsorbing CO2. Cellulose-based porous carbon has emerged as an outstanding candidate adsorbent for CO2 capture. The research involved the creation of hierarchically nitrogen-doped porous carbon (HNC) using a 3D cellulose alcogel (CA) as substrate, which was prepared through a thermal introduced phase separation (TIPS) method combined with hydrolysis treatment. The plentiful functional groups of CA offered abundant growth sites for the introduction of ZIF-8 crystals. CA/ZIF-8 composite alcogel (CZA) with greater surface area and superior thermal stability compared to CA was successfully fabricated and used as precursor for the production of HNC. The important effects of microporosity and surface chemistry of HNC samples on CO2 capture were discussed in-depth. HNC-350–850 displayed a hierarchically porous structure with a large number of micropores and abundant N/O/Zn-doped functional groups, exhibiting high CO2 uptakes at 1 bar (3.56 mmol/g at 25 °C), good IAST CO2/N2 (15/85, V/V) selectivity of 16.31 at 25 °C, and outstanding regenerability with ∼100 % CO2 adsorption capacity retained after ten cycles. In the two-component CO2/N2 (15/85, V/V) competitive adsorption process, the maximum breakthrough periods for CO2 (433.1 s) is significantly longer than that of N2 (3.8 s). The CO2 breakthrough adsorption capacity reached 1.79 mmol/g at 25 °C and 1 bar with the separation coefficient of 45.8 for CO2 over N2. These results confirmed that HNC, in addition to having excellent CO2/N2 selectivity, demonstrated good feasibility for practical use. The green and practicable synthetic route described in this study is not only expected to offer novel adsorbents for high performance CO2 capture, but also provide a new theoretical foundation for the development of cellulose-based porous materials.

The process optimization and exergy efficiency analysis for biogas to renewable hydrogen by chemical looping technology

The study on biogas-Chemical Looping Hydrogen Production (biogas-CLHP) highlights its potential to produce high-purity hydrogen with recycling waste bioenergy. It delves into the gas-solid reaction characteristics and optimizes crucial process parameters for the fixed-bed CLHP system. Movement rate equations for the reaction fronts of Fe2O3-Fe3O4 (Rf1), Fe3O4-FeO (Rf2), and FeO-Fe (Rf3) are formulated, aiding in clarifying CO breakthrough curves. The identified convergence temperatures (Tm) under CO and H2 atmospheres are 916 °C and 907 °C, respectively. Rf1 and Rf2 converged above Tm, which leads to the disappearance of the Fe3O4 region in oxygen carrier (OC) beds. A theoretical model for calculating the reduction solid conversion (Xaver) is established, accurately characterizing Xaver under varying reaction times, H2/CO ratios, and temperatures. By using the Particle Swarm Optimization (PSO) algorithm and thermodynamic analysis, the study determines the optimal length-radius ratio (L/r), reduction temperature (T), and H2/CO ratio (α) for the OC bed as 12.5, 916 °C, and 0.72, respectively, at which a maximum conversion ratio of 43.55 % is achieved. Based on optimal parameters, the results of Aspen simulation reveal energy and exergy efficiencies of 74.9 % and 70.8 %. Generally, this research comprehensively outlines the process dynamics, optimizes key parameters, and evaluates exergy efficiency of the biogas-CLHP system.

Feasibility of CO2 storage and enhanced gas recovery in depleted tight sandstone gas reservoirs within multi-stage fracturing horizontal wells

Injecting CO2 when the gas reservoir of tight sandstone is depleted can achieve the dual purposes of greenhouse gas storage and enhanced gas recovery (CS-EGR). To evaluate the feasibility of CO2 injection to enhance gas recovery and understand the production mechanism, a numerical simulation model of CS-EGR in multi-stage fracturing horizontal wells is established. The behavior of gas production and CO2 sequestration is then analyzed through numerical simulation, and the impact of fracture parameters on production performance is examined. Simulation results show that the production rate increases significantly and a large amount of CO2 is stored in the reservoir, proving the technical potential. However, hydraulic fractures accelerate CO2 breakthrough, resulting in lower gas recovery and lower CO2 storage than in gas reservoirs without fracturing. Increasing the length of hydraulic fractures can significantly increase CH4 production, but CO2 breakthrough will advance. Staggered and spaced perforation of hydraulic fractures in injection wells and production wells changes the fluid flow path, which can delay CO2 breakthrough and benefit production efficiency. The fracture network of massive hydraulic fracturing has a positive effect on the CS-EGR. As a result, CH4 production, gas recovery, and CO2 storage increase with the increase in stimulated reservoir volume.

A novel dual-network CO2-responsive particle gel for mitigating CO2 channeling and leakage in hydrocarbon recovery and carbon storage

In this work, we developed and systematically evaluated a novel dual-network CO2-responsive preformed particle gel (DN-CRPPG) to address CO2 channeling and leakage in hydrocarbon recovery and carbon sequestration. The work was proven an effective effort to overcome the insufficiency of traditional channeling-control chemicals due to severe dehydration and degradation in contact with CO2. We focused on placement and plugging behavior of the DN-CRPPG in fractured reservoirs under various circumstances. Results of high-pressure vessel test show that DN-CRPPG expanded further when exposed to supercritical CO2. The responsive swelling ability was enhanced with the increase of pressure. Core flooding and plugging experiments show significantly improved CO2-channeling control ability compared with traditional non-responsive particle gels. Three key mechanisms were observed to be responsible for the improved performance. First, the CO2-responsive behavior resulted in volume swelling of DN-CRPPG. Therefore, the void spaces among the particles were occupied by the swelled gel particles, making the DN-CRPPG exhibit an in-situ self-reinforcement behavior in plugging ability. Second, the dual network structure with a relatively rigid network and a more flexible one enhances its mechanical ability and resistance to CO2 flush. Third, the CO2-repsonsiveness enabled the new gel a remarkably higher resistance to CO2 than that to water, while traditional gel is the opposite. This behavior is favorable for balancing the mobility of CO2 and water as the fact that CO2 itself is much more mobile due to low viscosities. Significant CO2 breakthrough mitigation and oil recovery improvement were demonstrated after treatment with the new gel (>30 %). This work is supposed to provide guidance for improving the success of hydrocarbon recovery and carbon storage by more effectively mitigating CO2 channeling and leakage.

Evaluating the efficacy of biogeochemical cover system in mitigating landfill gas emissions: A large-scale laboratory simulation.

Municipal solid waste (MSW) landfills are a significant source of methane (CH4) emissions in the United States, contributing to global warming. Current landfill gas (LFG) management methods, like the landfill cover system and LFG collection system, do not entirely prevent LFG release. Biocovers have the potential to reduce CH4 emissions through microbial oxidation. However, LFG also contains carbon dioxide (CO2) and trace hydrogen sulfide (H2S) depending on waste composition, temperature, moisture content, and age of waste. An innovative biogeochemical cover (BGCC) was developed to tackle these concerns. This cover comprises a biochar-based biocover layer overlaid with a basic oxygen furnace (BOF) steel slag layer. The biochar-based biocover layer oxidizes CH4 emissions, while the BOF slag layer reduces CO2 and H2S through carbonation and sulfidation reaction mechanisms. The BGCC system's field performance remains unexamined. Therefore, a large-scale tank setup simulating near-field conditions was developed to evaluate the BGCC system's ability to mitigate CH4, CO2, and H2S from LFG simultaneously. Synthetic LFG was passed through the BGCC in five distinct phases, each designed to simulate the varying gas compositions and flux rates typical of MSW landfill. Gas profiles along the depth were monitored during each phase, and gas removal efficiency was measured. After testing, biocover and BOF slag samples were extracted to analyze physico-chemical properties. Batch tests were also conducted on samples extracted from the biocover and BOF slag layers to determine potential CH4 oxidation rates and residual CO2 sequestration capacity. The results showed that the BGCC system's CH4 removal efficiency decreased with higher CH4 flux rates, achieving its highest removal (74.7-79.7%) at moderate influx rates (23.9-25.5g CH4/m2-day) and reducing to its lowest removal (27.4%) at the highest influx rate (57.5g CH4/m2-day). Complete H2S removal occurred during Phase 3 in the biocover layer of BGCC system. CH4 oxidation rates were highest near the upper (277.9µg CH4/g-day) and lowest in the deeper region of the biocover layer. In the tank experiment, CO2 breakthrough occurred after 156days due to drying of the BOF slag layer, with an average residual carbonation capacity of 46 gCO2/kg slag after moisture adjustment. Overall, the BGCC system effectively mitigated LFG emissions, including CH4, CO2, and H2S, at moderate flux rates, showing promise as a comprehensive solution for LFG management.

Improving monitoring network design to detect leaks at hazardous facilities: Lessons from a CO2 storage site

Exploring the challenges posed by uncertainties in numerical modeling for hazardous material storage, this study introduces methodologies to improve monitoring networks for detecting subsurface leakages. The proposed approaches were applied to the Korea CO2 Storage Environmental Management (K-COSEM) test site, undergoing calibration, validation and uncertainty analysis through hydraulic and controlled-CO2 release tests. The calibration phase involved inter-well tracer and multi-well pumping tests, leveraging the Parameter ESTimation (PEST) model to determine the aquifer flow and solute transport properties of the K-COSEM site. To tackle uncertainties with limited observation data, we adopted Latin Hypercube simulation. Our uncertainty analysis confirmed model accuracy in simulating observed CO2 breakthrough curves. We also explored a probabilistic method to identify the environmental change point (EnCP) through correlation analysis with the distance from the CO2 injection well, revealing a linear trend and pinpointed potential preferential flow pathways by assessing detection probabilities. Evaluating CO2 detection capabilities was crucial for optimizing monitoring well placement, highlighting strategic well selection based on detection probabilities. This study advances managing uncertainties in hydrogeological modeling, underscoring the importance of sophisticated models in designing monitoring networks for hazardous leak detection in complex subsurface conditions.

Physicochemical behavior and impact of CO2 and CH4 plumes during gas-rich water leakage in a shallow carbonate freshwater aquifer

Carbon capture and storage (CCS) is a promising technology for reducing CO2 emissions. Significant concerns have emerged about the potential leakage of CO2 into shallow aquifers, highlighting the risk to water quality and environmental safety. This underscores the importance of finding monitoring tools suitable for different geological scenarios. If leakage occurs in the context of depleted reservoirs being used for CO2 storage, residual CH4 from the storage complex will likely be entrained together with CO2. However, few studies have addressed the implications of CH4 presence and its potential as a monitoring parameter during CO2 leakage.To address this gap, we simulated a leakage event by injecting water enriched with CO2 and CH4 into a shallow limestone aquifer. The impact of the injection was monitored using a combination of laboratory measurements on water samples and in-situ sensors located downstream from the injection well.All parameters were affected by the simulated leakage. Some monitoring tools allowed us to differentiate the leakage event from natural variations. A key finding of this study was that at 7 m from the injection well, the CH4 breakthrough occurred roughly one day before the CO2 breakthrough, highlighting the potential of CH4 as an early indicator of CO2 leakage and suggesting interesting prospectives for industrial-scale sites. However, further research is needed to confirm the potential of CH4 as a CO2 leakage indicator at industrial scales, due to potential methane oxidation and loss of the signal with longer times and distances.This study contributes to a better understanding of the potential risks and effective monitoring strategies associated with CO2-CH4 leakage in carbonate aquifers.

Analysis of rock microstructure after CO2 breakthrough based on CT scanning

Analysis of rock microstructure after CO2 breakthrough based on CT scanning

The Feasibility of Heat Extraction Using CO2 in the Carbonate Reservoir in Shandong Province, China

CO2 is being considered as an effective alternative working fluid for geothermal applications due to its superior fluid dynamics and heat transfer properties compared to water. Utilizing sedimentary rocks for geothermal energy recovery through a CO2-plume geothermal system, especially in carbonate reservoirs, has been shown to be a practical approach that eliminates the need for hydraulic fracturing. However, uncertainties remain regarding the thermal and hydraulic behavior, particularly the chemical interactions between CO2 and carbonate rocks. This study develops a comprehensive wellbore–reservoir coupling reactive transport model based on specific information obtained from the Ordovician limestone geothermal reservoir in Shandong, China. The model aims to assess the feasibility of heat extraction in carbonate reservoirs by evaluating the heat extraction performance and fluid–rock interaction. The results indicate a rapid temperature drop after CO2 breakthrough due to the Joule–Thomson effect. Simultaneously, the fluid transitions into and maintains a two-phase state throughout the operation. Chemical reactions within the reservoir are not aggressive since complete mixing between unsaturated water and CO2 only occurs in the vicinity of the production well, highlighting the potential of utilizing carbonate reservoirs for efficient heat extraction in geothermal systems. Further research is needed to optimize the performance of CO2-based geothermal systems in carbonate reservoirs.

Numerical simulation study of natural gas hydrate extraction by depressurization combined with CO2 replacement

Utilizing depressurization combined with CO2 replacement for extracting natural gas hydrates (NGHs) is vital for sustainable energy and carbon sequestration. In this study, a NGH reservoir numerical model was developed to investigate the impacts of depressurization combined with CO2 replacement on hydrate extraction at a field scale. Findings reveal an increase in methane production with reduced bottom hole pressure. When the bottom hole pressure is 3.5 MPa, the methane production reaches its maximum at 1.71 × 106 m3. Methane hydrates near production and injection wells decompose first, with deeper hydrates decomposing more easily. Higher differential pressures between injection and production enhance methane extraction and CO2 hydrate formation. However, excessive pressure differential (1.5 MPa) can lead to CO2 breakthrough across reservoir barriers, adversely affecting the purity of methane. At 1800 d, the methane purity is only 36.2 %. Optimal methane extraction and CO2 sequestration occur at higher injection temperatures (9 °C) below the CO2 hydrate phase equilibrium temperature. Temperatures above this equilibrium adversely affect both processes. The study validates the feasibility of depressurization with CO2 replacement for extracting hydrates in the Shenhu area, offering theoretical guidance for synergistic offshore methane extraction and CO2 sequestration techniques.

CO2 injection-based enhanced methane recovery from carbonate gas reservoirs via deep learning

CO2 injection is a promising technology for enhancing gas recovery (CO2-EGR) that concomitantly reduces carbon emissions and aids the energy transition, although it has not yet been applied commercially at the field scale. We develop an innovative workflow using raw data to provide an effective approach in evaluating CH4 recovery during CO2-EGR. A well-calibrated three-dimensional geological model is generated and validated using actual field data—achieving a robust alignment between history and simulation. We visualize the spread of the CO2 plume and quantitatively evaluate the dynamic productivity to the single gas well. We use three deep learning algorithms to predict the time histories of CO2 rate and CH4 recovery and provide feedback on production wells across various injection systems. The results indicate that CO2 injection can enhance CH4 recovery in water-bearing gas reservoirs—CH4 recovery increases with injection rate escalating. Specifically, the increased injection rate diminishes CO2 breakthrough time while concurrently expanding the swept area. The increased injection rate reduces CO2 breakthrough time and increases the swept area. Deep learning algorithms exhibit superior predictive performance, with the gated recurrent unit model being the most reliable and fastest among the three algorithms, particularly when accommodating injection and production time series, as evidenced by its smallest values for evaluation metrics. This study provides an efficient method for predicting the dynamic productivity before and after CO2 injection, which exhibits a speedup that is 3–4 orders of magnitudes higher than traditional numerical simulation. Such models show promise in advancing the practical application of CO2-EGR technology in gas reservoir development.

Comparative study on different coals from the Lorraine basin (France) by sorption isotherms, thermogravimetric analysis and breakthrough curves for CO2-ECBM recovery

The enhanced coalbed methane recovery using CO2 injection (CO2-ECBM) is widely proposed as a way of achieving the energy transition and reducing atmospheric CO2 in areas such as the Lorrain basin in France, where heavy industry is responsible for huge CO2 emissions and coal mines have been closed for more than a decade. This paper deals with the feasibility of extracting methane from the Lorraine basin using CO2-ECBM by comparing data from sorption isotherms, thermogravimetric analyses and breakthrough curves for two coal samples. One is bituminous (Box 18), from Folschviller (France) and is compared with another sub-bituminous (TH01) from La Houve (France), which is used as a reference because it was identified as a good candidate for CO2-ECBM in a previous research program. The quantities of adsorbed gases (CO2/CH4) obtained by sorption isotherms, thermogravimetry and CO2 breakthrough curves showed that Box 18 adsorbs more CO2 and CH4 than TH01 due to its higher porosity and good affinity for gases (CO2/CH4). Tόth model fits the experimental CH4 and CO2 adsorption isotherms better, reflecting the fact that the adsorption surface of the coals studied is heterogeneous. Adsorption enthalpies obtained by calorimetry indicated physisorption for gas-coal interactions, with higher values for CO2 than for CH4. Thermogravimetric analyses and breakthrough curves carried out at up to 50% relative humidity showed that the adsorption capacity of CO2 decreases with increasing temperature and the presence of water, respectively. The compilation of these experimental data explained the adsorption process of the studied coals and revealed their advantages for CO2-ECBM.

A Combined Neural Network Forecasting Approach for CO2-Enhanced Shale Gas Recovery

Summary Intensive growth of geological carbon sequestration has motivated the energy sector to diversify its storage portfolios, given the background of climate change mitigation. As an abundant unconventional reserve, shale gas reservoirs play a critical role in providing sufficient energy supply and geological carbon storage potentials. However, the low recovery factors of the primary recovery stage are a major concern during reservoir operations. Although injecting CO2 can resolve the dual challenges of improving the recovery factors and storing CO2 permanently, forecasting the reservoir performance heavily relies on reservoir simulation, which is a time-consuming process. In recent years, pioneered studies demonstrated that using machine learning (ML) algorithms can make predictions in an accurate and timely manner but fails to capture the time-series and spatial features of operational realities. In this work, we carried out a novel combinational framework including the artificial neural network (ANN, i.e., multilayer perceptron or MLP) and long short-term memory (LSTM) or bi-directional LSTM (Bi-LSTM) algorithms, tackling the challenges mentioned before. In addition, the deployment of ML algorithms in the petroleum industry is insufficient because of the field data shortage. Here, we also demonstrated an approach for synthesizing field-specific data sets using a numerical method. The findings of this work can be articulated from three perspectives. First, the cumulative gas recovery factor can be improved by 6% according to the base reservoir model with input features of the Barnett shale, whereas the CO2 retention factor sharply declined to 40% after the CO2 breakthrough. Second, using combined ANN and LSTM (ANN-LSTM)/Bi-LSTM is a feasible alternative to reservoir simulation that can be around 120 times faster than the numerical approach. By comparing an evaluation matrix of algorithms, we observed that trade-offs exist between computational time and accuracy in selecting different algorithms. This work provides fundamental support to the shale gas industry in developing comparable ML-based tools to replace traditional numerical simulation in a timely manner.

Dynamic of CO2 adsorption in a fixed bed of microporous and mesoporous activated carbon impregnated with sodium hydroxide and the application of response surface methodology (RSM) for determining optimal adsorption conditions.

Microporous and mesoporous activated carbon produced from longan-seed biomass were impregnated with NaOH and used to capture CO2 from a simulated flue gas in a fixed-bed column. The process variables that were studied included types of activated carbon as characterized by the volume ratio of micropores and mesopores (Vmic/Vmes), adsorption temperature, NaOH loading, gas feed rate and the adsorbent amount. All five process variables affected the two important breakthrough parameters, namely the breakthrough time (tB) and CO2 adsorption capacity at breakthrough time (qB), with different trends and degrees. However, it was only the NaOH loading that showed a characteristic of an optimum loading that provided the maximum of the breakthrough parameters. It was found that an approximate 45% increase in the adsorbed amount of CO2 could be achieved with the activated carbon impregnated with around 1 weight % NaOH solution as compared to the case of the non-impregnated carbon. The response surface methodology (RSM) was applied to develop the correlations for both tB and qB and the maximum predicted qB of 33.58mg/g was derived at the NaOH loading of 76.5mg/g carbon, Vmic/Vmes of 2.83, adsorption temperature of 20°C, gas feed rate of 156kg/m2-h and adsorbent amount of 51kg/m2 of column cross-section area. The Klinkenberg's breakthrough model was able to describe the CO2 breakthrough curves reasonably well for all the tested conditions. The analysis of the two model parameters, the affinity constant (K) and the effective pore diffusivity (De), revealed that the optimum Vmic/Vmes that provided the maximum K value was around 2.90, corresponding to the activated carbon that contains 74% and 26% by volume of micropores and mesopores, respectively. The proper volume ratio of micropores and mesopores along with alkali addition into activated carbon can be effectively used for maximizing CO2 adsorption in a fixed-bed adsorption system.

An application of a genetic algorithm in co-optimization of geological CO2 storage based on artificial neural networks

Abstract Global warming, driven by human-induced disruptions to the natural carbon dioxide (CO2) cycle, is a pressing concern. To mitigate this, carbon capture and storage has emerged as a key strategy that enables the continued use of fossil fuels while transitioning to cleaner energy sources. Deep saline aquifers are of particular interest due to their substantial CO2 storage potential, often located near fossil fuel reservoirs. In this study, a deep saline aquifer model with a saline water production well was constructed to develop the optimization workflow. Due to the time-consuming nature of each realization of the numerical simulation, we introduce a surrogate aquifer model derived from extracted data. The novelty of our work lies in the pioneering of simultaneous optimization using machine learning within an integrated framework. Unlike previous studies, which typically focused on single-parameter optimization, our research addresses this gap by performing multi-objective optimization for CO2 storage and breakthrough time in deep saline aquifers using a data-driven model. Our methodology encompasses preprocessing and feature selection, identifying eight pivotal parameters. Evaluation metrics include root mean square error (RMSE), mean absolute percentage error (MAPE) and R2. In predicting CO2 storage values, RMSE, MAPE and R2 in test data were 2.07%, 1.52% and 0.99, respectively, while in blind data, they were 2.5%, 2.05% and 0.99. For the CO2 breakthrough time, RMSE, MAPE and R2 in the test data were 2.1%, 1.77% and 0.93, while in the blind data they were 2.8%, 2.23% and 0.92, respectively. In addressing the substantial computational demands and time-consuming nature of coupling a numerical simulator with an optimization algorithm, we have adopted a strategy in which the trained artificial neural network is seamlessly integrated with a multi-objective genetic algorithm. Within this framework, we conducted 5000 comprehensive experiments to rigorously validate the development of the Pareto front, highlighting the depth of our computational approach. The findings of the study promise insights into the interplay between CO2 breakthrough time and storage in aquifer-based carbon capture and storage processes within an integrated framework based on data-driven coupled multi-objective optimization.

Green synthesis approach using deep eutectic solvents to enhance the surface functional groups on porous carbon for CO2 capture

This study explores the potential of green solvents using amino acids-based deep eutectic solvents to alter surface functionality of activated carbon thus enhancing the carbon dioxide (CO2) adsorption capacity. Green solvent is prepared by mixing an amino acid (L-Arginine) with ethylene glycol to form amino acid-based deep eutectic solvents. Amino acid-based deep eutectic solvents were used to modify the surface functionalities of activated carbon derived from palm shell waste. The change in surface functional groups and surface morphology of the modified activated carbon samples were characterized by Fourier-transform infrared spectroscopy and Scanning electron microscopy-energy dispersive X-ray analysis. Then, CO2 removal performance was performed using a packed-bed CO2 adsorption reactor to evaluate CO2 breakthrough time and adsorption capacity. CO2 adsorption experiments were measured at a certain temperature (25–45°C), at a fixed feed flow rate and CO2 concentration of 200 mL/min and 15%. It was observed that modified activated carbon showed the highest breakthrough time (15.2 min) compared to raw palm shell (5.2 min) at an adsorption temperature of 25°C. CO2 breakthrough times significantly decreased with increasing adsorption temperature because of physical adsorption.

Performance and dynamic behavior of H2 layered-bed PSA processes using various activated carbons and zeolite LiX for steam methane reforming gas

Layered-bed pressure swing adsorption (PSA) processes using activated carbons (ACs) and zeolite LiX were investigated to elucidate the performance and dynamic behavior of each AC in H2 separation. Experimental adsorption and re-adsorption breakthrough characteristics of ACs (CAC: parent AC, KACa: KOH-activated CAC, and KACi: KOH-impregnated CAC) and zeolite LiX were compared using a steam methane reforming gas. A rigorous dynamic model was used to evaluate the performance of four and six layered-bed PSA processes in terms of purity, recovery, productivity, and impurities. The KACi bed demonstrated the longest breakthrough time of impurities, but its desorption performance was adversely affected by the strong affinity induced by intercalated potassium compounds. While KACa showed higher adsorption capacities than CAC, its slow adsorption rate resulted in the shortest breakthrough time. On the other hand, CAC presented a separation performance between KACi and KACa beds, but its weak CO adsorption affinity led to a relatively wide CO mass transfer zone. Zeolite LiX demonstrated the longest CO breakthrough time, indicating its potential to purify trace CO in the layered-bed, but its layer length must be carefully decided to prevent CO2 propagation. The four layered-bed PSA produced a fuel cell grade H2 (99.9999% purity, 81.54% recovery, 115 mol/kg/day productivity, and < 0.2 ppm CO), which was further improved by the six layered-bed PSA to 1.5% at the same H2 purity. The dynamic behaviors and sensitivity analysis (P/F ratio, adsorption time, AC/LiX ratio, AC type, and number of beds) provide valuable insights and guidelines for the design and optimization of H2 layered-bed PSA.

Stacked nano FAU zeolite as hierarchical Murray material for enhancing CO2 diffusion kinetics

Zeolite materials are widely used in the field of carbon capture and storage (CCS) due to their high specific surface area, good stability, and low cost. However, the microporous structure of zeolite is not conducive to the mass transfer and diffusion processes, which limits the efficiency and practical application of CO2 capture. In this study, we designed a hierarchical Murray material consisting of ultra-small particle size (<20 nm) zeolite stacks based on Murray's law to enhance the diffusion kinetics of CO2. The results demonstrate that the material exhibits a CO2 adsorption capacity of 107.07 cm3·g−1 and a CO2/N2 selectivity of 120.58, meeting the requirements for high-performance CO2 capture materials. Kinetic CO2 adsorption and breakthrough experiments confirm that the nano-FAU zeolite enables fast and efficient CO2 capture compared to micron-sized zeolite. The stacked nano-zeolites with hierarchical pore structure not only shorten the diffusion path but also facilitate the surface permeation process, which contributes to improved diffusion kinetics. This work provides a valuable strategy for the development of CO2 adsorption materials with high mass transfer rates.

CO2-Induced Gate-Opening Adsorption on a Chabazite/Phillipsite Composite Zeolite Transformed from a Faujasite Zeolite Using Organic Structure-Directing Agent-Free Steam-Assisted Conversion.

Organic structure-directing agent-free steam-assisted conversion and Cs+ ion exchange were used to transform the faujasite (FAU)-type zeolite to the Cs+-type chabazite/phillipsite (CHA/PHI) composite zeolite. Compared with the pure PHI-type zeolite, the Cs+-type CHA/PHI zeolite showed gate-opening CO2 adsorption behavior and good thermal stability. In situ powder X-ray diffraction (PXRD) of the CO2 adsorption was measured to elucidate the mechanism for the gate-opening adsorption on the CHA/PHI zeolite. The Na+-type CHA/PHI zeolite did not show such adsorption behavior, and the PXRD pattern of the Na+-type CHA/PHI zeolite did not change with increasing CO2 partial pressure, which suggests that this unique adsorption behavior was caused by the PHI framework transition or Cs+ ions moving in both the CHA and PHI frameworks. Furthermore, in situ Fourier-transform infrared spectra of CO2 adsorption and CO2 breakthrough measurement on the Cs+-type CHA/PHI zeolite suggest that the CHA and PHI frameworks in the CHA/PHI zeolite shared eight-membered-ring windows and that CO2 molecules could easily diffuse from a CHA cage to a PHI framework. The ideal adsorbed solution theory was used to calculate the CO2/N2 separation selectivity for the Cs+-type CHA/PHI zeolite. At 298 and 318 K, the Cs+-type CHA/PHI composite zeolite showed a high CO2/N2 separation coefficient of >10,000 compared with other zeolites with high CO2 adsorption capacity. Furthermore, the CO2 working capacity was calculated for the Cs+-type CHA/PHI zeolite in both the pressure- and temperature-swing processes, and the results showed that the CHA/PHI composite zeolite could selectively separate CO2 from the CO2/N2 gas mixtures released from power generation plants operating using these processes.